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We examine 3D reconstruction of architectural scenes in unordered sets ofuncalibrated images. We introduce a linear method to self-calibrate and findthe metric reconstruction of a camera pair. We assume unknown and differentfocal lengths but otherwise known internal camera parameters and a knownprojective reconstruction of the camera pair. We recover two possible cameraconfigurations in space and use the Cheirality condition, that all 3D scenepoints are in front of both cameras, to disambiguate the solution. We show intwo Theorems, first that the two solutions are in mirror positions and then therelations between their viewing directions. Our new method performs on par(median rotation error $\Delta R = 3.49^{\circ}$) with the standard approach ofKruppa equations ($\Delta R = 3.77^{\circ}$) for self-calibration and 5-Pointalgorithm for calibrated metric reconstruction of a camera pair. We rejecterroneous image correspondences by introducing a method to examine whetherpoint correspondences appear in the same order along $x, y$ image axes in imagepairs. We evaluate this method by its precision and recall and show that itimproves the robustness of point matches in architectural and general scenes.Finally, we integrate all the introduced methods to a 3D reconstructionpipeline. We utilize the numerous camera pair metric recontructions usingrotation-averaging algorithms and a novel method to average focal lengthestimates.

A linear method for camera pair self-calibration and multi-view reconstruction with geometrically verified correspondences